{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kaggle比赛\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该比赛的网页地址是 https://www.kaggle.com/c/house-prices-advanced-regression-techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4.0\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)\n",
    "torch.set_default_tensor_type(torch.FloatTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取和读取数据集\n",
    "# 比赛数据分为训练数据集和测试数据集。两个数据集都包括每栋房子的特征，如街道类型、建造年份、房顶类型、地下室状况等特征值。\n",
    "# 这些特征值有连续的数字、离散的标签甚至是缺失值“na”。\n",
    "# 只有训练数据集包括了每栋房子的价格，也就是标签\n",
    "\n",
    "# 该比赛的数据集包括两个csv文件，使用pandas读取这两个文件\n",
    "prefix = \"Datasets/House_Prices/\"\n",
    "train_data = pd.read_csv(prefix + 'train.csv')\n",
    "test_data = pd.read_csv(prefix + 'test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练数据集包括1460个样本、80个特征和1个标签\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试数据集包括1459个样本和80个特征,没有标签\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage SaleType SaleCondition  SalePrice\n",
       "0   1          60       RL         65.0       WD        Normal     208500\n",
       "1   2          20       RL         80.0       WD        Normal     181500\n",
       "2   3          60       RL         68.0       WD        Normal     223500\n",
       "3   4          70       RL         60.0       WD       Abnorml     140000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看前4个样本的前4个特征、后2个特征和标签（SalePrice）\n",
    "train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0             60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1             20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2             60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3             70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4             60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...          ...      ...          ...      ...    ...   ...      ...   \n",
       "1454         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "1            Lvl    AllPub       FR2  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "3            Lvl    AllPub    Corner  ...           0        0    NaN    NaN   \n",
       "4            Lvl    AllPub       FR2  ...           0        0    NaN    NaN   \n",
       "...          ...       ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub    Inside  ...           0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0       2    2008        WD         Normal  \n",
       "1            NaN       0       5    2007        WD         Normal  \n",
       "2            NaN       0       9    2008        WD         Normal  \n",
       "3            NaN       0       2    2006        WD        Abnorml  \n",
       "4            NaN       0      12    2008        WD         Normal  \n",
       "...          ...     ...     ...     ...       ...            ...  \n",
       "1454         NaN       0       6    2006        WD         Normal  \n",
       "1455         NaN       0       4    2006        WD        Abnorml  \n",
       "1456         NaN       0       9    2006        WD        Abnorml  \n",
       "1457        Shed     700       7    2006        WD         Normal  \n",
       "1458         NaN       0      11    2006        WD         Normal  \n",
       "\n",
       "[2919 rows x 79 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第一个特征是Id，它能帮助模型记住每个训练样本，但难以推广到测试样本，不使用它来训练\n",
    "\n",
    "all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))\n",
    "all_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 预处理数据\n",
    "对连续数值的特征做标准化（standardization）：设该特征在整个数据集上的均值为μ，标准差为σ。那么，我们可以将该特征的每个值先减去μ再除以σ得到标准化后的每个特征值。对于缺失的特征值，我们将其替换成该特征的均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
      "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
      "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
      "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
      "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
      "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
      "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
      "       'MoSold', 'YrSold'],\n",
      "      dtype='object') (36,)\n"
     ]
    }
   ],
   "source": [
    "numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index\n",
    "print(numeric_features, numeric_features.shape)\n",
    "all_features[numeric_features] = all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std()))\n",
    "# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值\n",
    "all_features[numeric_features] = all_features[numeric_features].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来将离散数值转成指示特征。举个例子，假设特征MSZoning里面有两个不同的离散值RL和RM，那么这一步转换将去掉MSZoning特征，并新加两个特征MSZoning_RL和MSZoning_RM，其值为0或1。如果一个样本原来在MSZoning里的值为RL，那么有MSZoning_RL=1且MSZoning_RM=0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 331)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征\n",
    "all_features = pd.get_dummies(all_features, dummy_na=True)\n",
    "all_features.shape # (2919, 331)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到这一步转换将特征数从79增加到了331"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_features:  torch.Size([1460, 331])\n",
      "test_features:  torch.Size([1459, 331])\n",
      "train_labels torch.Size([1460, 1])\n"
     ]
    }
   ],
   "source": [
    "# 最后，通过values属性得到NumPy格式的数据，\n",
    "# 并转成Tensor方便后面的训练\n",
    "\n",
    "n_train = train_data.shape[0]\n",
    "features_train = torch.tensor(all_features[:n_train].values, dtype=torch.float)\n",
    "features_test = torch.tensor(all_features[n_train:].values, dtype=torch.float)\n",
    "labels_train = torch.tensor(train_data.SalePrice.values, dtype=torch.float).view(-1, 1)\n",
    "\n",
    "print('train_features: ', features_train.shape)\n",
    "print('test_features: ', features_test.shape)\n",
    "print('train_labels', labels_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义线性回归模型 和 平方损失函数\n",
    "def get_net(feature_num):\n",
    "    net = nn.Linear(feature_num, 1)\n",
    "    for param in net.parameters():\n",
    "        nn.init.normal_(param, mean=0, std=0.01)\n",
    "    return net\n",
    "\n",
    "loss = torch.nn.MSELoss()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数均方根误差的实现\n",
    "def log_rmse(net, features, labels):\n",
    "    with torch.no_grad():\n",
    "        # 将小于1的值设成1，使得取对数时数值更稳定\n",
    "        clipped_preds = torch.max(net(features), torch.tensor(1.0))\n",
    "        rmse = torch.sqrt(loss(clipped_preds.log(), labels.log()))\n",
    "    return rmse.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义训练函数，使用Adam优化算法\n",
    "# 相对于小批量随机梯度下降，它对学习率不那么敏感\n",
    "\n",
    "def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):\n",
    "    train_ls, test_ls = [], []\n",
    "    dataset = torch.utils.data.TensorDataset(train_features, train_labels)\n",
    "    train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)\n",
    "    optimizer = torch.optim.Adam(params=net.parameters(), lr=learning_rate, weight_decay=weight_decay)\n",
    "    net = net.float()\n",
    "    for epoch in range(num_epochs):\n",
    "        for X, y in train_iter:\n",
    "            l = loss(net(X.float()), y.float())\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "        train_ls.append(log_rmse(net, train_features, train_labels))\n",
    "        if test_labels is not None:\n",
    "            test_ls.append(log_rmse(net, test_features, test_labels))\n",
    "    return train_ls, test_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K折交叉验证，用来选择模型设计并调节超参数\n",
    "# 下面实现了一个函数，它返回第i折交叉验证时所需要的训练和验证数据\n",
    "\n",
    "def get_k_fold_data(k, i, X, y):\n",
    "    assert k > 1\n",
    "    fold_size = X.shape[0] // k\n",
    "    X_train, y_train = None, None\n",
    "    for j in range(k):\n",
    "        idx = slice(j * fold_size, (j+1) * fold_size)\n",
    "        X_part, y_part = X[idx, :], y[idx]\n",
    "        if j == i:\n",
    "            X_valid, y_valid = X_part, y_part\n",
    "        elif X_train is None:\n",
    "            X_train, y_train = X_part, y_part\n",
    "        else:\n",
    "            X_train = torch.cat((X_train, X_part), dim=0)\n",
    "            y_train = torch.cat((y_train, y_part), dim=0)\n",
    "    return X_train, y_train, X_valid, y_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#画图所需要的函数\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg') # 用矢量图显示\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize # 设置图的尺寸\n",
    "\n",
    "def semilogy(x_vals, y_vals, x_label, y_label,\n",
    "            x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)):\n",
    "    set_figsize(figsize)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.semilogy(x_vals, y_vals)\n",
    "    if x2_vals and y2_vals:\n",
    "        plt.semilogy(x2_vals, y2_vals, linestyle=\":\")\n",
    "        plt.legend(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在K折交叉验证中，训练K次并返回训练和验证的平均误差\n",
    "\n",
    "def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):\n",
    "    train_ls_sum, valid_ls_sum = 0.0, 0.0\n",
    "    for i in range(K):\n",
    "        data = get_k_fold_data(K, i, X_train, y_train)\n",
    "        net = get_net(X_train.shape[1])\n",
    "        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate, weight_decay, batch_size)\n",
    "        train_ls_sum += train_ls[-1]\n",
    "        valid_ls_sum += valid_ls[-1]\n",
    "        if i == 0:\n",
    "            semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'rmse',\n",
    "                    range(1, num_epochs+1), valid_ls, ['train','valid'])\n",
    "        print('fold %d, train_rmse %f, valid_rmse %f' % (i, train_ls[-1], valid_ls[-1]))\n",
    "    return train_ls_sum/K, valid_ls_sum/K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0, train_rmse 0.170724, valid_rmse 0.157327\n",
      "fold 1, train_rmse 0.161782, valid_rmse 0.187625\n",
      "fold 2, train_rmse 0.163751, valid_rmse 0.168297\n",
      "fold 3, train_rmse 0.167994, valid_rmse 0.154991\n",
      "fold 4, train_rmse 0.162889, valid_rmse 0.182989\n",
      "5-fold validation: avg train_rmse 0.165428, avg valid_rmse 0.170246\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n",
       "<svg height=\"180.65625pt\" version=\"1.1\" viewBox=\"0 0 248.644602 180.65625\" width=\"248.644602pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       " <defs>\r\n",
       "  <style type=\"text/css\">\r\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\r\n",
       "  </style>\r\n",
       " </defs>\r\n",
       " <g id=\"figure_1\">\r\n",
       "  <g id=\"patch_1\">\r\n",
       "   <path d=\"M 0 180.65625 \r\n",
       "L 248.644602 180.65625 \r\n",
       "L 248.644602 0 \r\n",
       "L 0 0 \r\n",
       "z\r\n",
       "\" style=\"fill:none;\"/>\r\n",
       "  </g>\r\n",
       "  <g id=\"axes_1\">\r\n",
       "   <g id=\"patch_2\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 240.778125 143.1 \r\n",
       "L 240.778125 7.2 \r\n",
       "L 45.478125 7.2 \r\n",
       "z\r\n",
       "\" style=\"fill:#ffffff;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_1\">\r\n",
       "    <g id=\"xtick_1\">\r\n",
       "     <g id=\"line2d_1\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L 0 3.5 \r\n",
       "\" id=\"mdcc991d7ec\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"52.562009\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_1\">\r\n",
       "      <!-- 0 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 31.78125 66.40625 \r\n",
       "Q 24.171875 66.40625 20.328125 58.90625 \r\n",
       "Q 16.5 51.421875 16.5 36.375 \r\n",
       "Q 16.5 21.390625 20.328125 13.890625 \r\n",
       "Q 24.171875 6.390625 31.78125 6.390625 \r\n",
       "Q 39.453125 6.390625 43.28125 13.890625 \r\n",
       "Q 47.125 21.390625 47.125 36.375 \r\n",
       "Q 47.125 51.421875 43.28125 58.90625 \r\n",
       "Q 39.453125 66.40625 31.78125 66.40625 \r\n",
       "z\r\n",
       "M 31.78125 74.21875 \r\n",
       "Q 44.046875 74.21875 50.515625 64.515625 \r\n",
       "Q 56.984375 54.828125 56.984375 36.375 \r\n",
       "Q 56.984375 17.96875 50.515625 8.265625 \r\n",
       "Q 44.046875 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.53125 -1.421875 13.0625 8.265625 \r\n",
       "Q 6.59375 17.96875 6.59375 36.375 \r\n",
       "Q 6.59375 54.828125 13.0625 64.515625 \r\n",
       "Q 19.53125 74.21875 31.78125 74.21875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-48\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(49.380759 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_2\">\r\n",
       "     <g id=\"line2d_2\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"88.429778\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_2\">\r\n",
       "      <!-- 20 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 19.1875 8.296875 \r\n",
       "L 53.609375 8.296875 \r\n",
       "L 53.609375 0 \r\n",
       "L 7.328125 0 \r\n",
       "L 7.328125 8.296875 \r\n",
       "Q 12.9375 14.109375 22.625 23.890625 \r\n",
       "Q 32.328125 33.6875 34.8125 36.53125 \r\n",
       "Q 39.546875 41.84375 41.421875 45.53125 \r\n",
       "Q 43.3125 49.21875 43.3125 52.78125 \r\n",
       "Q 43.3125 58.59375 39.234375 62.25 \r\n",
       "Q 35.15625 65.921875 28.609375 65.921875 \r\n",
       "Q 23.96875 65.921875 18.8125 64.3125 \r\n",
       "Q 13.671875 62.703125 7.8125 59.421875 \r\n",
       "L 7.8125 69.390625 \r\n",
       "Q 13.765625 71.78125 18.9375 73 \r\n",
       "Q 24.125 74.21875 28.421875 74.21875 \r\n",
       "Q 39.75 74.21875 46.484375 68.546875 \r\n",
       "Q 53.21875 62.890625 53.21875 53.421875 \r\n",
       "Q 53.21875 48.921875 51.53125 44.890625 \r\n",
       "Q 49.859375 40.875 45.40625 35.40625 \r\n",
       "Q 44.1875 33.984375 37.640625 27.21875 \r\n",
       "Q 31.109375 20.453125 19.1875 8.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-50\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(82.067278 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_3\">\r\n",
       "     <g id=\"line2d_3\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"124.297546\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_3\">\r\n",
       "      <!-- 40 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 37.796875 64.3125 \r\n",
       "L 12.890625 25.390625 \r\n",
       "L 37.796875 25.390625 \r\n",
       "z\r\n",
       "M 35.203125 72.90625 \r\n",
       "L 47.609375 72.90625 \r\n",
       "L 47.609375 25.390625 \r\n",
       "L 58.015625 25.390625 \r\n",
       "L 58.015625 17.1875 \r\n",
       "L 47.609375 17.1875 \r\n",
       "L 47.609375 0 \r\n",
       "L 37.796875 0 \r\n",
       "L 37.796875 17.1875 \r\n",
       "L 4.890625 17.1875 \r\n",
       "L 4.890625 26.703125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-52\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(117.935046 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_4\">\r\n",
       "     <g id=\"line2d_4\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"160.165315\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_4\">\r\n",
       "      <!-- 60 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 33.015625 40.375 \r\n",
       "Q 26.375 40.375 22.484375 35.828125 \r\n",
       "Q 18.609375 31.296875 18.609375 23.390625 \r\n",
       "Q 18.609375 15.53125 22.484375 10.953125 \r\n",
       "Q 26.375 6.390625 33.015625 6.390625 \r\n",
       "Q 39.65625 6.390625 43.53125 10.953125 \r\n",
       "Q 47.40625 15.53125 47.40625 23.390625 \r\n",
       "Q 47.40625 31.296875 43.53125 35.828125 \r\n",
       "Q 39.65625 40.375 33.015625 40.375 \r\n",
       "z\r\n",
       "M 52.59375 71.296875 \r\n",
       "L 52.59375 62.3125 \r\n",
       "Q 48.875 64.0625 45.09375 64.984375 \r\n",
       "Q 41.3125 65.921875 37.59375 65.921875 \r\n",
       "Q 27.828125 65.921875 22.671875 59.328125 \r\n",
       "Q 17.53125 52.734375 16.796875 39.40625 \r\n",
       "Q 19.671875 43.65625 24.015625 45.921875 \r\n",
       "Q 28.375 48.1875 33.59375 48.1875 \r\n",
       "Q 44.578125 48.1875 50.953125 41.515625 \r\n",
       "Q 57.328125 34.859375 57.328125 23.390625 \r\n",
       "Q 57.328125 12.15625 50.6875 5.359375 \r\n",
       "Q 44.046875 -1.421875 33.015625 -1.421875 \r\n",
       "Q 20.359375 -1.421875 13.671875 8.265625 \r\n",
       "Q 6.984375 17.96875 6.984375 36.375 \r\n",
       "Q 6.984375 53.65625 15.1875 63.9375 \r\n",
       "Q 23.390625 74.21875 37.203125 74.21875 \r\n",
       "Q 40.921875 74.21875 44.703125 73.484375 \r\n",
       "Q 48.484375 72.75 52.59375 71.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-54\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(153.802815 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_5\">\r\n",
       "     <g id=\"line2d_5\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"196.033084\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_5\">\r\n",
       "      <!-- 80 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 31.78125 34.625 \r\n",
       "Q 24.75 34.625 20.71875 30.859375 \r\n",
       "Q 16.703125 27.09375 16.703125 20.515625 \r\n",
       "Q 16.703125 13.921875 20.71875 10.15625 \r\n",
       "Q 24.75 6.390625 31.78125 6.390625 \r\n",
       "Q 38.8125 6.390625 42.859375 10.171875 \r\n",
       "Q 46.921875 13.96875 46.921875 20.515625 \r\n",
       "Q 46.921875 27.09375 42.890625 30.859375 \r\n",
       "Q 38.875 34.625 31.78125 34.625 \r\n",
       "z\r\n",
       "M 21.921875 38.8125 \r\n",
       "Q 15.578125 40.375 12.03125 44.71875 \r\n",
       "Q 8.5 49.078125 8.5 55.328125 \r\n",
       "Q 8.5 64.0625 14.71875 69.140625 \r\n",
       "Q 20.953125 74.21875 31.78125 74.21875 \r\n",
       "Q 42.671875 74.21875 48.875 69.140625 \r\n",
       "Q 55.078125 64.0625 55.078125 55.328125 \r\n",
       "Q 55.078125 49.078125 51.53125 44.71875 \r\n",
       "Q 48 40.375 41.703125 38.8125 \r\n",
       "Q 48.828125 37.15625 52.796875 32.3125 \r\n",
       "Q 56.78125 27.484375 56.78125 20.515625 \r\n",
       "Q 56.78125 9.90625 50.3125 4.234375 \r\n",
       "Q 43.84375 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.734375 -1.421875 13.25 4.234375 \r\n",
       "Q 6.78125 9.90625 6.78125 20.515625 \r\n",
       "Q 6.78125 27.484375 10.78125 32.3125 \r\n",
       "Q 14.796875 37.15625 21.921875 38.8125 \r\n",
       "z\r\n",
       "M 18.3125 54.390625 \r\n",
       "Q 18.3125 48.734375 21.84375 45.5625 \r\n",
       "Q 25.390625 42.390625 31.78125 42.390625 \r\n",
       "Q 38.140625 42.390625 41.71875 45.5625 \r\n",
       "Q 45.3125 48.734375 45.3125 54.390625 \r\n",
       "Q 45.3125 60.0625 41.71875 63.234375 \r\n",
       "Q 38.140625 66.40625 31.78125 66.40625 \r\n",
       "Q 25.390625 66.40625 21.84375 63.234375 \r\n",
       "Q 18.3125 60.0625 18.3125 54.390625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-56\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(189.670584 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_6\">\r\n",
       "     <g id=\"line2d_6\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"231.900852\" xlink:href=\"#mdcc991d7ec\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_6\">\r\n",
       "      <!-- 100 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 12.40625 8.296875 \r\n",
       "L 28.515625 8.296875 \r\n",
       "L 28.515625 63.921875 \r\n",
       "L 10.984375 60.40625 \r\n",
       "L 10.984375 69.390625 \r\n",
       "L 28.421875 72.90625 \r\n",
       "L 38.28125 72.90625 \r\n",
       "L 38.28125 8.296875 \r\n",
       "L 54.390625 8.296875 \r\n",
       "L 54.390625 0 \r\n",
       "L 12.40625 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-49\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(222.357102 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_7\">\r\n",
       "     <!-- epochs -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 56.203125 29.59375 \r\n",
       "L 56.203125 25.203125 \r\n",
       "L 14.890625 25.203125 \r\n",
       "Q 15.484375 15.921875 20.484375 11.0625 \r\n",
       "Q 25.484375 6.203125 34.421875 6.203125 \r\n",
       "Q 39.59375 6.203125 44.453125 7.46875 \r\n",
       "Q 49.3125 8.734375 54.109375 11.28125 \r\n",
       "L 54.109375 2.78125 \r\n",
       "Q 49.265625 0.734375 44.1875 -0.34375 \r\n",
       "Q 39.109375 -1.421875 33.890625 -1.421875 \r\n",
       "Q 20.796875 -1.421875 13.15625 6.1875 \r\n",
       "Q 5.515625 13.8125 5.515625 26.8125 \r\n",
       "Q 5.515625 40.234375 12.765625 48.109375 \r\n",
       "Q 20.015625 56 32.328125 56 \r\n",
       "Q 43.359375 56 49.78125 48.890625 \r\n",
       "Q 56.203125 41.796875 56.203125 29.59375 \r\n",
       "z\r\n",
       "M 47.21875 32.234375 \r\n",
       "Q 47.125 39.59375 43.09375 43.984375 \r\n",
       "Q 39.0625 48.390625 32.421875 48.390625 \r\n",
       "Q 24.90625 48.390625 20.390625 44.140625 \r\n",
       "Q 15.875 39.890625 15.1875 32.171875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-101\"/>\r\n",
       "      <path d=\"M 18.109375 8.203125 \r\n",
       "L 18.109375 -20.796875 \r\n",
       "L 9.078125 -20.796875 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.390625 \r\n",
       "Q 20.953125 51.265625 25.265625 53.625 \r\n",
       "Q 29.59375 56 35.59375 56 \r\n",
       "Q 45.5625 56 51.78125 48.09375 \r\n",
       "Q 58.015625 40.1875 58.015625 27.296875 \r\n",
       "Q 58.015625 14.40625 51.78125 6.484375 \r\n",
       "Q 45.5625 -1.421875 35.59375 -1.421875 \r\n",
       "Q 29.59375 -1.421875 25.265625 0.953125 \r\n",
       "Q 20.953125 3.328125 18.109375 8.203125 \r\n",
       "z\r\n",
       "M 48.6875 27.296875 \r\n",
       "Q 48.6875 37.203125 44.609375 42.84375 \r\n",
       "Q 40.53125 48.484375 33.40625 48.484375 \r\n",
       "Q 26.265625 48.484375 22.1875 42.84375 \r\n",
       "Q 18.109375 37.203125 18.109375 27.296875 \r\n",
       "Q 18.109375 17.390625 22.1875 11.75 \r\n",
       "Q 26.265625 6.109375 33.40625 6.109375 \r\n",
       "Q 40.53125 6.109375 44.609375 11.75 \r\n",
       "Q 48.6875 17.390625 48.6875 27.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-112\"/>\r\n",
       "      <path d=\"M 30.609375 48.390625 \r\n",
       "Q 23.390625 48.390625 19.1875 42.75 \r\n",
       "Q 14.984375 37.109375 14.984375 27.296875 \r\n",
       "Q 14.984375 17.484375 19.15625 11.84375 \r\n",
       "Q 23.34375 6.203125 30.609375 6.203125 \r\n",
       "Q 37.796875 6.203125 41.984375 11.859375 \r\n",
       "Q 46.1875 17.53125 46.1875 27.296875 \r\n",
       "Q 46.1875 37.015625 41.984375 42.703125 \r\n",
       "Q 37.796875 48.390625 30.609375 48.390625 \r\n",
       "z\r\n",
       "M 30.609375 56 \r\n",
       "Q 42.328125 56 49.015625 48.375 \r\n",
       "Q 55.71875 40.765625 55.71875 27.296875 \r\n",
       "Q 55.71875 13.875 49.015625 6.21875 \r\n",
       "Q 42.328125 -1.421875 30.609375 -1.421875 \r\n",
       "Q 18.84375 -1.421875 12.171875 6.21875 \r\n",
       "Q 5.515625 13.875 5.515625 27.296875 \r\n",
       "Q 5.515625 40.765625 12.171875 48.375 \r\n",
       "Q 18.84375 56 30.609375 56 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-111\"/>\r\n",
       "      <path d=\"M 48.78125 52.59375 \r\n",
       "L 48.78125 44.1875 \r\n",
       "Q 44.96875 46.296875 41.140625 47.34375 \r\n",
       "Q 37.3125 48.390625 33.40625 48.390625 \r\n",
       "Q 24.65625 48.390625 19.8125 42.84375 \r\n",
       "Q 14.984375 37.3125 14.984375 27.296875 \r\n",
       "Q 14.984375 17.28125 19.8125 11.734375 \r\n",
       "Q 24.65625 6.203125 33.40625 6.203125 \r\n",
       "Q 37.3125 6.203125 41.140625 7.25 \r\n",
       "Q 44.96875 8.296875 48.78125 10.40625 \r\n",
       "L 48.78125 2.09375 \r\n",
       "Q 45.015625 0.34375 40.984375 -0.53125 \r\n",
       "Q 36.96875 -1.421875 32.421875 -1.421875 \r\n",
       "Q 20.0625 -1.421875 12.78125 6.34375 \r\n",
       "Q 5.515625 14.109375 5.515625 27.296875 \r\n",
       "Q 5.515625 40.671875 12.859375 48.328125 \r\n",
       "Q 20.21875 56 33.015625 56 \r\n",
       "Q 37.15625 56 41.109375 55.140625 \r\n",
       "Q 45.0625 54.296875 48.78125 52.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-99\"/>\r\n",
       "      <path d=\"M 54.890625 33.015625 \r\n",
       "L 54.890625 0 \r\n",
       "L 45.90625 0 \r\n",
       "L 45.90625 32.71875 \r\n",
       "Q 45.90625 40.484375 42.875 44.328125 \r\n",
       "Q 39.84375 48.1875 33.796875 48.1875 \r\n",
       "Q 26.515625 48.1875 22.3125 43.546875 \r\n",
       "Q 18.109375 38.921875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 75.984375 \r\n",
       "L 18.109375 75.984375 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.34375 51.125 25.703125 53.5625 \r\n",
       "Q 30.078125 56 35.796875 56 \r\n",
       "Q 45.21875 56 50.046875 50.171875 \r\n",
       "Q 54.890625 44.34375 54.890625 33.015625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-104\"/>\r\n",
       "      <path d=\"M 44.28125 53.078125 \r\n",
       "L 44.28125 44.578125 \r\n",
       "Q 40.484375 46.53125 36.375 47.5 \r\n",
       "Q 32.28125 48.484375 27.875 48.484375 \r\n",
       "Q 21.1875 48.484375 17.84375 46.4375 \r\n",
       "Q 14.5 44.390625 14.5 40.28125 \r\n",
       "Q 14.5 37.15625 16.890625 35.375 \r\n",
       "Q 19.28125 33.59375 26.515625 31.984375 \r\n",
       "L 29.59375 31.296875 \r\n",
       "Q 39.15625 29.25 43.1875 25.515625 \r\n",
       "Q 47.21875 21.78125 47.21875 15.09375 \r\n",
       "Q 47.21875 7.46875 41.1875 3.015625 \r\n",
       "Q 35.15625 -1.421875 24.609375 -1.421875 \r\n",
       "Q 20.21875 -1.421875 15.453125 -0.5625 \r\n",
       "Q 10.6875 0.296875 5.421875 2 \r\n",
       "L 5.421875 11.28125 \r\n",
       "Q 10.40625 8.6875 15.234375 7.390625 \r\n",
       "Q 20.0625 6.109375 24.8125 6.109375 \r\n",
       "Q 31.15625 6.109375 34.5625 8.28125 \r\n",
       "Q 37.984375 10.453125 37.984375 14.40625 \r\n",
       "Q 37.984375 18.0625 35.515625 20.015625 \r\n",
       "Q 33.0625 21.96875 24.703125 23.78125 \r\n",
       "L 21.578125 24.515625 \r\n",
       "Q 13.234375 26.265625 9.515625 29.90625 \r\n",
       "Q 5.8125 33.546875 5.8125 39.890625 \r\n",
       "Q 5.8125 47.609375 11.28125 51.796875 \r\n",
       "Q 16.75 56 26.8125 56 \r\n",
       "Q 31.78125 56 36.171875 55.265625 \r\n",
       "Q 40.578125 54.546875 44.28125 53.078125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-115\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(125.295312 171.376563)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"61.523438\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "      <use x=\"125\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "      <use x=\"186.181641\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"241.162109\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "      <use x=\"304.541016\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_2\">\r\n",
       "    <g id=\"ytick_1\">\r\n",
       "     <g id=\"line2d_7\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L -3.5 0 \r\n",
       "\" id=\"mafd7633857\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"45.478125\" xlink:href=\"#mafd7633857\" y=\"64.812685\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_8\">\r\n",
       "      <!-- $\\mathdefault{10^{0}}$ -->\r\n",
       "      <g transform=\"translate(20.878125 68.611904)scale(0.1 -0.1)\">\r\n",
       "       <use transform=\"translate(0 0.765625)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use transform=\"translate(63.623047 0.765625)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use transform=\"translate(128.203125 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_2\">\r\n",
       "     <g id=\"line2d_8\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L -2 0 \r\n",
       "\" id=\"md5dee73cc0\" style=\"stroke:#000000;stroke-width:0.6;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"127.481588\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_3\">\r\n",
       "     <g id=\"line2d_9\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"111.693434\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_4\">\r\n",
       "     <g id=\"line2d_10\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"100.491561\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_5\">\r\n",
       "     <g id=\"line2d_11\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"91.802713\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_6\">\r\n",
       "     <g id=\"line2d_12\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"84.703407\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_7\">\r\n",
       "     <g id=\"line2d_13\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"78.701029\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_8\">\r\n",
       "     <g id=\"line2d_14\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"73.501533\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_9\">\r\n",
       "     <g id=\"line2d_15\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"68.915253\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_10\">\r\n",
       "     <g id=\"line2d_16\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"37.822658\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_11\">\r\n",
       "     <g id=\"line2d_17\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"22.034504\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_12\">\r\n",
       "     <g id=\"line2d_18\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#md5dee73cc0\" y=\"10.832631\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_9\">\r\n",
       "     <!-- rmse -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 41.109375 46.296875 \r\n",
       "Q 39.59375 47.171875 37.8125 47.578125 \r\n",
       "Q 36.03125 48 33.890625 48 \r\n",
       "Q 26.265625 48 22.1875 43.046875 \r\n",
       "Q 18.109375 38.09375 18.109375 28.8125 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 20.953125 51.171875 25.484375 53.578125 \r\n",
       "Q 30.03125 56 36.53125 56 \r\n",
       "Q 37.453125 56 38.578125 55.875 \r\n",
       "Q 39.703125 55.765625 41.0625 55.515625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-114\"/>\r\n",
       "      <path d=\"M 52 44.1875 \r\n",
       "Q 55.375 50.25 60.0625 53.125 \r\n",
       "Q 64.75 56 71.09375 56 \r\n",
       "Q 79.640625 56 84.28125 50.015625 \r\n",
       "Q 88.921875 44.046875 88.921875 33.015625 \r\n",
       "L 88.921875 0 \r\n",
       "L 79.890625 0 \r\n",
       "L 79.890625 32.71875 \r\n",
       "Q 79.890625 40.578125 77.09375 44.375 \r\n",
       "Q 74.3125 48.1875 68.609375 48.1875 \r\n",
       "Q 61.625 48.1875 57.5625 43.546875 \r\n",
       "Q 53.515625 38.921875 53.515625 30.90625 \r\n",
       "L 53.515625 0 \r\n",
       "L 44.484375 0 \r\n",
       "L 44.484375 32.71875 \r\n",
       "Q 44.484375 40.625 41.703125 44.40625 \r\n",
       "Q 38.921875 48.1875 33.109375 48.1875 \r\n",
       "Q 26.21875 48.1875 22.15625 43.53125 \r\n",
       "Q 18.109375 38.875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.1875 51.21875 25.484375 53.609375 \r\n",
       "Q 29.78125 56 35.6875 56 \r\n",
       "Q 41.65625 56 45.828125 52.96875 \r\n",
       "Q 50 49.953125 52 44.1875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-109\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(14.798437 87.75625)rotate(-90)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"41.097656\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"138.509766\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "      <use x=\"190.609375\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"line2d_19\">\r\n",
       "    <path clip-path=\"url(#pb6d7c84828)\" d=\"M 54.355398 13.524545 \r\n",
       "L 56.148786 21.436939 \r\n",
       "L 57.942175 26.88301 \r\n",
       "L 59.735563 31.236877 \r\n",
       "L 61.528951 34.976332 \r\n",
       "L 63.32234 38.277592 \r\n",
       "L 65.115728 41.280005 \r\n",
       "L 66.909117 44.056098 \r\n",
       "L 68.702505 46.671792 \r\n",
       "L 70.495894 49.144667 \r\n",
       "L 72.289282 51.503339 \r\n",
       "L 74.08267 53.754134 \r\n",
       "L 75.876059 55.941315 \r\n",
       "L 77.669447 58.062935 \r\n",
       "L 79.462836 60.123822 \r\n",
       "L 81.256224 62.138201 \r\n",
       "L 83.049613 64.091982 \r\n",
       "L 84.843001 66.015959 \r\n",
       "L 86.636389 67.898927 \r\n",
       "L 88.429778 69.770215 \r\n",
       "L 90.223166 71.604247 \r\n",
       "L 92.016555 73.414483 \r\n",
       "L 93.809943 75.193699 \r\n",
       "L 95.603332 76.95031 \r\n",
       "L 97.39672 78.670148 \r\n",
       "L 99.190108 80.403898 \r\n",
       "L 100.983497 82.123905 \r\n",
       "L 102.776885 83.843546 \r\n",
       "L 104.570274 85.52398 \r\n",
       "L 106.363662 87.190892 \r\n",
       "L 108.157051 88.821995 \r\n",
       "L 109.950439 90.460371 \r\n",
       "L 111.743827 92.072209 \r\n",
       "L 113.537216 93.679152 \r\n",
       "L 115.330604 95.262558 \r\n",
       "L 117.123993 96.813596 \r\n",
       "L 118.917381 98.399875 \r\n",
       "L 120.71077 99.937393 \r\n",
       "L 122.504158 101.42785 \r\n",
       "L 124.297546 102.905433 \r\n",
       "L 126.090935 104.365234 \r\n",
       "L 127.884323 105.793385 \r\n",
       "L 129.677712 107.218144 \r\n",
       "L 131.4711 108.645805 \r\n",
       "L 133.264489 109.994414 \r\n",
       "L 135.057877 111.343796 \r\n",
       "L 136.851265 112.63301 \r\n",
       "L 138.644654 113.903125 \r\n",
       "L 140.438042 115.132436 \r\n",
       "L 142.231431 116.294994 \r\n",
       "L 144.024819 117.467151 \r\n",
       "L 145.818208 118.540229 \r\n",
       "L 147.611596 119.60323 \r\n",
       "L 149.404985 120.626326 \r\n",
       "L 151.198373 121.607478 \r\n",
       "L 152.991761 122.526591 \r\n",
       "L 154.78515 123.445227 \r\n",
       "L 156.578538 124.266587 \r\n",
       "L 158.371927 125.089332 \r\n",
       "L 160.165315 125.82507 \r\n",
       "L 161.958704 126.53267 \r\n",
       "L 163.752092 127.195887 \r\n",
       "L 165.54548 127.821467 \r\n",
       "L 167.338869 128.401319 \r\n",
       "L 169.132257 128.923317 \r\n",
       "L 170.925646 129.398966 \r\n",
       "L 172.719034 129.852783 \r\n",
       "L 174.512423 130.256576 \r\n",
       "L 176.305811 130.631851 \r\n",
       "L 178.099199 130.991505 \r\n",
       "L 179.892588 131.302876 \r\n",
       "L 181.685976 131.597379 \r\n",
       "L 183.479365 131.858353 \r\n",
       "L 185.272753 132.09803 \r\n",
       "L 187.066142 132.322848 \r\n",
       "L 188.85953 132.501411 \r\n",
       "L 190.652918 132.661988 \r\n",
       "L 192.446307 132.812314 \r\n",
       "L 194.239695 132.942816 \r\n",
       "L 196.033084 133.067725 \r\n",
       "L 197.826472 133.165374 \r\n",
       "L 199.619861 133.255653 \r\n",
       "L 201.413249 133.335314 \r\n",
       "L 203.206637 133.410614 \r\n",
       "L 205.000026 133.46944 \r\n",
       "L 206.793414 133.525079 \r\n",
       "L 208.586803 133.576652 \r\n",
       "L 210.380191 133.606536 \r\n",
       "L 212.17358 133.628035 \r\n",
       "L 213.966968 133.666306 \r\n",
       "L 215.760356 133.694116 \r\n",
       "L 217.553745 133.699608 \r\n",
       "L 219.347133 133.730316 \r\n",
       "L 221.140522 133.711904 \r\n",
       "L 222.93391 133.707526 \r\n",
       "L 224.727299 133.711151 \r\n",
       "L 226.520687 133.687405 \r\n",
       "L 228.314075 133.678217 \r\n",
       "L 230.107464 133.664052 \r\n",
       "L 231.900852 133.644362 \r\n",
       "\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"line2d_20\">\r\n",
       "    <path clip-path=\"url(#pb6d7c84828)\" d=\"M 54.355398 13.377273 \r\n",
       "L 56.148786 21.255435 \r\n",
       "L 57.942175 26.676026 \r\n",
       "L 59.735563 31.009586 \r\n",
       "L 61.528951 34.722453 \r\n",
       "L 63.32234 38.011004 \r\n",
       "L 65.115728 40.995526 \r\n",
       "L 66.909117 43.754546 \r\n",
       "L 68.702505 46.357123 \r\n",
       "L 70.495894 48.817178 \r\n",
       "L 72.289282 51.15984 \r\n",
       "L 74.08267 53.39836 \r\n",
       "L 75.876059 55.573442 \r\n",
       "L 77.669447 57.68355 \r\n",
       "L 79.462836 59.732297 \r\n",
       "L 81.256224 61.737522 \r\n",
       "L 83.049613 63.676688 \r\n",
       "L 84.843001 65.587707 \r\n",
       "L 86.636389 67.458423 \r\n",
       "L 88.429778 69.315315 \r\n",
       "L 90.223166 71.138 \r\n",
       "L 92.016555 72.93563 \r\n",
       "L 93.809943 74.70344 \r\n",
       "L 95.603332 76.452708 \r\n",
       "L 97.39672 78.167531 \r\n",
       "L 99.190108 79.892954 \r\n",
       "L 100.983497 81.607869 \r\n",
       "L 102.776885 83.321408 \r\n",
       "L 104.570274 85.000362 \r\n",
       "L 106.363662 86.666013 \r\n",
       "L 108.157051 88.301254 \r\n",
       "L 109.950439 89.941942 \r\n",
       "L 111.743827 91.561511 \r\n",
       "L 113.537216 93.179159 \r\n",
       "L 115.330604 94.77371 \r\n",
       "L 117.123993 96.340586 \r\n",
       "L 118.917381 97.945106 \r\n",
       "L 120.71077 99.504682 \r\n",
       "L 122.504158 101.023541 \r\n",
       "L 124.297546 102.532558 \r\n",
       "L 126.090935 104.032209 \r\n",
       "L 127.884323 105.501583 \r\n",
       "L 129.677712 106.972043 \r\n",
       "L 131.4711 108.450236 \r\n",
       "L 133.264489 109.852676 \r\n",
       "L 135.057877 111.269147 \r\n",
       "L 136.851265 112.628473 \r\n",
       "L 138.644654 113.97279 \r\n",
       "L 140.438042 115.286348 \r\n",
       "L 142.231431 116.533047 \r\n",
       "L 144.024819 117.79579 \r\n",
       "L 145.818208 118.967364 \r\n",
       "L 147.611596 120.129954 \r\n",
       "L 149.404985 121.264098 \r\n",
       "L 151.198373 122.359583 \r\n",
       "L 152.991761 123.393399 \r\n",
       "L 154.78515 124.433222 \r\n",
       "L 156.578538 125.372599 \r\n",
       "L 158.371927 126.321522 \r\n",
       "L 160.165315 127.172181 \r\n",
       "L 161.958704 128.003203 \r\n",
       "L 163.752092 128.787402 \r\n",
       "L 165.54548 129.534565 \r\n",
       "L 167.338869 130.234055 \r\n",
       "L 169.132257 130.866017 \r\n",
       "L 170.925646 131.452373 \r\n",
       "L 172.719034 132.009581 \r\n",
       "L 174.512423 132.507919 \r\n",
       "L 176.305811 132.972642 \r\n",
       "L 178.099199 133.429128 \r\n",
       "L 179.892588 133.81899 \r\n",
       "L 181.685976 134.194343 \r\n",
       "L 183.479365 134.528321 \r\n",
       "L 185.272753 134.828328 \r\n",
       "L 187.066142 135.122523 \r\n",
       "L 188.85953 135.350832 \r\n",
       "L 190.652918 135.558554 \r\n",
       "L 192.446307 135.755588 \r\n",
       "L 194.239695 135.910538 \r\n",
       "L 196.033084 136.070867 \r\n",
       "L 197.826472 136.20498 \r\n",
       "L 199.619861 136.328805 \r\n",
       "L 201.413249 136.435727 \r\n",
       "L 203.206637 136.524468 \r\n",
       "L 205.000026 136.597423 \r\n",
       "L 206.793414 136.668631 \r\n",
       "L 208.586803 136.725511 \r\n",
       "L 210.380191 136.772186 \r\n",
       "L 212.17358 136.805368 \r\n",
       "L 213.966968 136.852282 \r\n",
       "L 215.760356 136.88779 \r\n",
       "L 217.553745 136.904715 \r\n",
       "L 219.347133 136.922727 \r\n",
       "L 221.140522 136.916542 \r\n",
       "L 222.93391 136.913193 \r\n",
       "L 224.727299 136.911915 \r\n",
       "L 226.520687 136.891802 \r\n",
       "L 228.314075 136.876186 \r\n",
       "L 230.107464 136.846725 \r\n",
       "L 231.900852 136.826565 \r\n",
       "\" style=\"fill:none;stroke:#ff7f0e;stroke-dasharray:1.5,2.475;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_3\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 45.478125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path d=\"M 240.778125 143.1 \r\n",
       "L 240.778125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_5\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 240.778125 143.1 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_6\">\r\n",
       "    <path d=\"M 45.478125 7.2 \r\n",
       "L 240.778125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"legend_1\">\r\n",
       "    <g id=\"patch_7\">\r\n",
       "     <path d=\"M 177.826562 44.55625 \r\n",
       "L 233.778125 44.55625 \r\n",
       "Q 235.778125 44.55625 235.778125 42.55625 \r\n",
       "L 235.778125 14.2 \r\n",
       "Q 235.778125 12.2 233.778125 12.2 \r\n",
       "L 177.826562 12.2 \r\n",
       "Q 175.826562 12.2 175.826562 14.2 \r\n",
       "L 175.826562 42.55625 \r\n",
       "Q 175.826562 44.55625 177.826562 44.55625 \r\n",
       "z\r\n",
       "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"line2d_21\">\r\n",
       "     <path d=\"M 179.826562 20.298437 \r\n",
       "L 199.826562 20.298437 \r\n",
       "\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"line2d_22\"/>\r\n",
       "    <g id=\"text_10\">\r\n",
       "     <!-- train -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 18.3125 70.21875 \r\n",
       "L 18.3125 54.6875 \r\n",
       "L 36.8125 54.6875 \r\n",
       "L 36.8125 47.703125 \r\n",
       "L 18.3125 47.703125 \r\n",
       "L 18.3125 18.015625 \r\n",
       "Q 18.3125 11.328125 20.140625 9.421875 \r\n",
       "Q 21.96875 7.515625 27.59375 7.515625 \r\n",
       "L 36.8125 7.515625 \r\n",
       "L 36.8125 0 \r\n",
       "L 27.59375 0 \r\n",
       "Q 17.1875 0 13.234375 3.875 \r\n",
       "Q 9.28125 7.765625 9.28125 18.015625 \r\n",
       "L 9.28125 47.703125 \r\n",
       "L 2.6875 47.703125 \r\n",
       "L 2.6875 54.6875 \r\n",
       "L 9.28125 54.6875 \r\n",
       "L 9.28125 70.21875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-116\"/>\r\n",
       "      <path d=\"M 34.28125 27.484375 \r\n",
       "Q 23.390625 27.484375 19.1875 25 \r\n",
       "Q 14.984375 22.515625 14.984375 16.5 \r\n",
       "Q 14.984375 11.71875 18.140625 8.90625 \r\n",
       "Q 21.296875 6.109375 26.703125 6.109375 \r\n",
       "Q 34.1875 6.109375 38.703125 11.40625 \r\n",
       "Q 43.21875 16.703125 43.21875 25.484375 \r\n",
       "L 43.21875 27.484375 \r\n",
       "z\r\n",
       "M 52.203125 31.203125 \r\n",
       "L 52.203125 0 \r\n",
       "L 43.21875 0 \r\n",
       "L 43.21875 8.296875 \r\n",
       "Q 40.140625 3.328125 35.546875 0.953125 \r\n",
       "Q 30.953125 -1.421875 24.3125 -1.421875 \r\n",
       "Q 15.921875 -1.421875 10.953125 3.296875 \r\n",
       "Q 6 8.015625 6 15.921875 \r\n",
       "Q 6 25.140625 12.171875 29.828125 \r\n",
       "Q 18.359375 34.515625 30.609375 34.515625 \r\n",
       "L 43.21875 34.515625 \r\n",
       "L 43.21875 35.40625 \r\n",
       "Q 43.21875 41.609375 39.140625 45 \r\n",
       "Q 35.0625 48.390625 27.6875 48.390625 \r\n",
       "Q 23 48.390625 18.546875 47.265625 \r\n",
       "Q 14.109375 46.140625 10.015625 43.890625 \r\n",
       "L 10.015625 52.203125 \r\n",
       "Q 14.9375 54.109375 19.578125 55.046875 \r\n",
       "Q 24.21875 56 28.609375 56 \r\n",
       "Q 40.484375 56 46.34375 49.84375 \r\n",
       "Q 52.203125 43.703125 52.203125 31.203125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-97\"/>\r\n",
       "      <path d=\"M 9.421875 54.6875 \r\n",
       "L 18.40625 54.6875 \r\n",
       "L 18.40625 0 \r\n",
       "L 9.421875 0 \r\n",
       "z\r\n",
       "M 9.421875 75.984375 \r\n",
       "L 18.40625 75.984375 \r\n",
       "L 18.40625 64.59375 \r\n",
       "L 9.421875 64.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-105\"/>\r\n",
       "      <path d=\"M 54.890625 33.015625 \r\n",
       "L 54.890625 0 \r\n",
       "L 45.90625 0 \r\n",
       "L 45.90625 32.71875 \r\n",
       "Q 45.90625 40.484375 42.875 44.328125 \r\n",
       "Q 39.84375 48.1875 33.796875 48.1875 \r\n",
       "Q 26.515625 48.1875 22.3125 43.546875 \r\n",
       "Q 18.109375 38.921875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.34375 51.125 25.703125 53.5625 \r\n",
       "Q 30.078125 56 35.796875 56 \r\n",
       "Q 45.21875 56 50.046875 50.171875 \r\n",
       "Q 54.890625 44.34375 54.890625 33.015625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-110\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(207.826562 23.798437)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"80.322266\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"141.601562\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "      <use x=\"169.384766\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"line2d_23\">\r\n",
       "     <path d=\"M 179.826562 34.976562 \r\n",
       "L 199.826562 34.976562 \r\n",
       "\" style=\"fill:none;stroke:#ff7f0e;stroke-dasharray:1.5,2.475;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"line2d_24\"/>\r\n",
       "    <g id=\"text_11\">\r\n",
       "     <!-- valid -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 2.984375 54.6875 \r\n",
       "L 12.5 54.6875 \r\n",
       "L 29.59375 8.796875 \r\n",
       "L 46.6875 54.6875 \r\n",
       "L 56.203125 54.6875 \r\n",
       "L 35.6875 0 \r\n",
       "L 23.484375 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-118\"/>\r\n",
       "      <path d=\"M 9.421875 75.984375 \r\n",
       "L 18.40625 75.984375 \r\n",
       "L 18.40625 0 \r\n",
       "L 9.421875 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-108\"/>\r\n",
       "      <path d=\"M 45.40625 46.390625 \r\n",
       "L 45.40625 75.984375 \r\n",
       "L 54.390625 75.984375 \r\n",
       "L 54.390625 0 \r\n",
       "L 45.40625 0 \r\n",
       "L 45.40625 8.203125 \r\n",
       "Q 42.578125 3.328125 38.25 0.953125 \r\n",
       "Q 33.9375 -1.421875 27.875 -1.421875 \r\n",
       "Q 17.96875 -1.421875 11.734375 6.484375 \r\n",
       "Q 5.515625 14.40625 5.515625 27.296875 \r\n",
       "Q 5.515625 40.1875 11.734375 48.09375 \r\n",
       "Q 17.96875 56 27.875 56 \r\n",
       "Q 33.9375 56 38.25 53.625 \r\n",
       "Q 42.578125 51.265625 45.40625 46.390625 \r\n",
       "z\r\n",
       "M 14.796875 27.296875 \r\n",
       "Q 14.796875 17.390625 18.875 11.75 \r\n",
       "Q 22.953125 6.109375 30.078125 6.109375 \r\n",
       "Q 37.203125 6.109375 41.296875 11.75 \r\n",
       "Q 45.40625 17.390625 45.40625 27.296875 \r\n",
       "Q 45.40625 37.203125 41.296875 42.84375 \r\n",
       "Q 37.203125 48.484375 30.078125 48.484375 \r\n",
       "Q 22.953125 48.484375 18.875 42.84375 \r\n",
       "Q 14.796875 37.203125 14.796875 27.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-100\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(207.826562 38.476562)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-118\"/>\r\n",
       "      <use x=\"59.179688\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"120.458984\" xlink:href=\"#DejaVuSans-108\"/>\r\n",
       "      <use x=\"148.242188\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "      <use x=\"176.025391\" xlink:href=\"#DejaVuSans-100\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"pb6d7c84828\">\r\n",
       "   <rect height=\"135.9\" width=\"195.3\" x=\"45.478125\" y=\"7.2\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "K = 5\n",
    "num_epochs = 100\n",
    "lr = 5\n",
    "weight_decay = 0\n",
    "batch_size = 64\n",
    "train_l, valid_l = k_fold(K, features_train, labels_train, num_epochs, lr, weight_decay, batch_size)\n",
    "print('%d-fold validation: avg train_rmse %f, avg valid_rmse %f' % (K, train_l, valid_l))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有时候会发现一组参数的训练误差可以达到很低，但是在KK折交叉验证上的误差可能反而较高。这种现象很可能是由过拟合造成的。因此，当训练误差降低时，要观察KK折交叉验证上的误差是否也相应降低。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse 0.162193\n",
      "        Id      SalePrice\n",
      "0     1461  119771.484375\n",
      "1     1462  154288.734375\n",
      "2     1463  198610.437500\n",
      "3     1464  217250.015625\n",
      "4     1465  177269.453125\n",
      "...    ...            ...\n",
      "1454  2915   74368.640625\n",
      "1455  2916   85509.343750\n",
      "1456  2917  208556.609375\n",
      "1457  2918  106859.890625\n",
      "1458  2919  240684.187500\n",
      "\n",
      "[1459 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n",
       "<svg height=\"180.65625pt\" version=\"1.1\" viewBox=\"0 0 248.644602 180.65625\" width=\"248.644602pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       " <defs>\r\n",
       "  <style type=\"text/css\">\r\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\r\n",
       "  </style>\r\n",
       " </defs>\r\n",
       " <g id=\"figure_1\">\r\n",
       "  <g id=\"patch_1\">\r\n",
       "   <path d=\"M 0 180.65625 \r\n",
       "L 248.644602 180.65625 \r\n",
       "L 248.644602 0 \r\n",
       "L 0 0 \r\n",
       "z\r\n",
       "\" style=\"fill:none;\"/>\r\n",
       "  </g>\r\n",
       "  <g id=\"axes_1\">\r\n",
       "   <g id=\"patch_2\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 240.778125 143.1 \r\n",
       "L 240.778125 7.2 \r\n",
       "L 45.478125 7.2 \r\n",
       "z\r\n",
       "\" style=\"fill:#ffffff;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_1\">\r\n",
       "    <g id=\"xtick_1\">\r\n",
       "     <g id=\"line2d_1\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L 0 3.5 \r\n",
       "\" id=\"m83ad5a60c6\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"52.562009\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_1\">\r\n",
       "      <!-- 0 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 31.78125 66.40625 \r\n",
       "Q 24.171875 66.40625 20.328125 58.90625 \r\n",
       "Q 16.5 51.421875 16.5 36.375 \r\n",
       "Q 16.5 21.390625 20.328125 13.890625 \r\n",
       "Q 24.171875 6.390625 31.78125 6.390625 \r\n",
       "Q 39.453125 6.390625 43.28125 13.890625 \r\n",
       "Q 47.125 21.390625 47.125 36.375 \r\n",
       "Q 47.125 51.421875 43.28125 58.90625 \r\n",
       "Q 39.453125 66.40625 31.78125 66.40625 \r\n",
       "z\r\n",
       "M 31.78125 74.21875 \r\n",
       "Q 44.046875 74.21875 50.515625 64.515625 \r\n",
       "Q 56.984375 54.828125 56.984375 36.375 \r\n",
       "Q 56.984375 17.96875 50.515625 8.265625 \r\n",
       "Q 44.046875 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.53125 -1.421875 13.0625 8.265625 \r\n",
       "Q 6.59375 17.96875 6.59375 36.375 \r\n",
       "Q 6.59375 54.828125 13.0625 64.515625 \r\n",
       "Q 19.53125 74.21875 31.78125 74.21875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-48\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(49.380759 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_2\">\r\n",
       "     <g id=\"line2d_2\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"88.429778\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_2\">\r\n",
       "      <!-- 20 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 19.1875 8.296875 \r\n",
       "L 53.609375 8.296875 \r\n",
       "L 53.609375 0 \r\n",
       "L 7.328125 0 \r\n",
       "L 7.328125 8.296875 \r\n",
       "Q 12.9375 14.109375 22.625 23.890625 \r\n",
       "Q 32.328125 33.6875 34.8125 36.53125 \r\n",
       "Q 39.546875 41.84375 41.421875 45.53125 \r\n",
       "Q 43.3125 49.21875 43.3125 52.78125 \r\n",
       "Q 43.3125 58.59375 39.234375 62.25 \r\n",
       "Q 35.15625 65.921875 28.609375 65.921875 \r\n",
       "Q 23.96875 65.921875 18.8125 64.3125 \r\n",
       "Q 13.671875 62.703125 7.8125 59.421875 \r\n",
       "L 7.8125 69.390625 \r\n",
       "Q 13.765625 71.78125 18.9375 73 \r\n",
       "Q 24.125 74.21875 28.421875 74.21875 \r\n",
       "Q 39.75 74.21875 46.484375 68.546875 \r\n",
       "Q 53.21875 62.890625 53.21875 53.421875 \r\n",
       "Q 53.21875 48.921875 51.53125 44.890625 \r\n",
       "Q 49.859375 40.875 45.40625 35.40625 \r\n",
       "Q 44.1875 33.984375 37.640625 27.21875 \r\n",
       "Q 31.109375 20.453125 19.1875 8.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-50\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(82.067278 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_3\">\r\n",
       "     <g id=\"line2d_3\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"124.297546\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_3\">\r\n",
       "      <!-- 40 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 37.796875 64.3125 \r\n",
       "L 12.890625 25.390625 \r\n",
       "L 37.796875 25.390625 \r\n",
       "z\r\n",
       "M 35.203125 72.90625 \r\n",
       "L 47.609375 72.90625 \r\n",
       "L 47.609375 25.390625 \r\n",
       "L 58.015625 25.390625 \r\n",
       "L 58.015625 17.1875 \r\n",
       "L 47.609375 17.1875 \r\n",
       "L 47.609375 0 \r\n",
       "L 37.796875 0 \r\n",
       "L 37.796875 17.1875 \r\n",
       "L 4.890625 17.1875 \r\n",
       "L 4.890625 26.703125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-52\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(117.935046 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_4\">\r\n",
       "     <g id=\"line2d_4\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"160.165315\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_4\">\r\n",
       "      <!-- 60 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 33.015625 40.375 \r\n",
       "Q 26.375 40.375 22.484375 35.828125 \r\n",
       "Q 18.609375 31.296875 18.609375 23.390625 \r\n",
       "Q 18.609375 15.53125 22.484375 10.953125 \r\n",
       "Q 26.375 6.390625 33.015625 6.390625 \r\n",
       "Q 39.65625 6.390625 43.53125 10.953125 \r\n",
       "Q 47.40625 15.53125 47.40625 23.390625 \r\n",
       "Q 47.40625 31.296875 43.53125 35.828125 \r\n",
       "Q 39.65625 40.375 33.015625 40.375 \r\n",
       "z\r\n",
       "M 52.59375 71.296875 \r\n",
       "L 52.59375 62.3125 \r\n",
       "Q 48.875 64.0625 45.09375 64.984375 \r\n",
       "Q 41.3125 65.921875 37.59375 65.921875 \r\n",
       "Q 27.828125 65.921875 22.671875 59.328125 \r\n",
       "Q 17.53125 52.734375 16.796875 39.40625 \r\n",
       "Q 19.671875 43.65625 24.015625 45.921875 \r\n",
       "Q 28.375 48.1875 33.59375 48.1875 \r\n",
       "Q 44.578125 48.1875 50.953125 41.515625 \r\n",
       "Q 57.328125 34.859375 57.328125 23.390625 \r\n",
       "Q 57.328125 12.15625 50.6875 5.359375 \r\n",
       "Q 44.046875 -1.421875 33.015625 -1.421875 \r\n",
       "Q 20.359375 -1.421875 13.671875 8.265625 \r\n",
       "Q 6.984375 17.96875 6.984375 36.375 \r\n",
       "Q 6.984375 53.65625 15.1875 63.9375 \r\n",
       "Q 23.390625 74.21875 37.203125 74.21875 \r\n",
       "Q 40.921875 74.21875 44.703125 73.484375 \r\n",
       "Q 48.484375 72.75 52.59375 71.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-54\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(153.802815 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_5\">\r\n",
       "     <g id=\"line2d_5\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"196.033084\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_5\">\r\n",
       "      <!-- 80 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 31.78125 34.625 \r\n",
       "Q 24.75 34.625 20.71875 30.859375 \r\n",
       "Q 16.703125 27.09375 16.703125 20.515625 \r\n",
       "Q 16.703125 13.921875 20.71875 10.15625 \r\n",
       "Q 24.75 6.390625 31.78125 6.390625 \r\n",
       "Q 38.8125 6.390625 42.859375 10.171875 \r\n",
       "Q 46.921875 13.96875 46.921875 20.515625 \r\n",
       "Q 46.921875 27.09375 42.890625 30.859375 \r\n",
       "Q 38.875 34.625 31.78125 34.625 \r\n",
       "z\r\n",
       "M 21.921875 38.8125 \r\n",
       "Q 15.578125 40.375 12.03125 44.71875 \r\n",
       "Q 8.5 49.078125 8.5 55.328125 \r\n",
       "Q 8.5 64.0625 14.71875 69.140625 \r\n",
       "Q 20.953125 74.21875 31.78125 74.21875 \r\n",
       "Q 42.671875 74.21875 48.875 69.140625 \r\n",
       "Q 55.078125 64.0625 55.078125 55.328125 \r\n",
       "Q 55.078125 49.078125 51.53125 44.71875 \r\n",
       "Q 48 40.375 41.703125 38.8125 \r\n",
       "Q 48.828125 37.15625 52.796875 32.3125 \r\n",
       "Q 56.78125 27.484375 56.78125 20.515625 \r\n",
       "Q 56.78125 9.90625 50.3125 4.234375 \r\n",
       "Q 43.84375 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.734375 -1.421875 13.25 4.234375 \r\n",
       "Q 6.78125 9.90625 6.78125 20.515625 \r\n",
       "Q 6.78125 27.484375 10.78125 32.3125 \r\n",
       "Q 14.796875 37.15625 21.921875 38.8125 \r\n",
       "z\r\n",
       "M 18.3125 54.390625 \r\n",
       "Q 18.3125 48.734375 21.84375 45.5625 \r\n",
       "Q 25.390625 42.390625 31.78125 42.390625 \r\n",
       "Q 38.140625 42.390625 41.71875 45.5625 \r\n",
       "Q 45.3125 48.734375 45.3125 54.390625 \r\n",
       "Q 45.3125 60.0625 41.71875 63.234375 \r\n",
       "Q 38.140625 66.40625 31.78125 66.40625 \r\n",
       "Q 25.390625 66.40625 21.84375 63.234375 \r\n",
       "Q 18.3125 60.0625 18.3125 54.390625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-56\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(189.670584 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_6\">\r\n",
       "     <g id=\"line2d_6\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"231.900852\" xlink:href=\"#m83ad5a60c6\" y=\"143.1\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_6\">\r\n",
       "      <!-- 100 -->\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 12.40625 8.296875 \r\n",
       "L 28.515625 8.296875 \r\n",
       "L 28.515625 63.921875 \r\n",
       "L 10.984375 60.40625 \r\n",
       "L 10.984375 69.390625 \r\n",
       "L 28.421875 72.90625 \r\n",
       "L 38.28125 72.90625 \r\n",
       "L 38.28125 8.296875 \r\n",
       "L 54.390625 8.296875 \r\n",
       "L 54.390625 0 \r\n",
       "L 12.40625 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-49\"/>\r\n",
       "      </defs>\r\n",
       "      <g transform=\"translate(222.357102 157.698438)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_7\">\r\n",
       "     <!-- epochs -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 56.203125 29.59375 \r\n",
       "L 56.203125 25.203125 \r\n",
       "L 14.890625 25.203125 \r\n",
       "Q 15.484375 15.921875 20.484375 11.0625 \r\n",
       "Q 25.484375 6.203125 34.421875 6.203125 \r\n",
       "Q 39.59375 6.203125 44.453125 7.46875 \r\n",
       "Q 49.3125 8.734375 54.109375 11.28125 \r\n",
       "L 54.109375 2.78125 \r\n",
       "Q 49.265625 0.734375 44.1875 -0.34375 \r\n",
       "Q 39.109375 -1.421875 33.890625 -1.421875 \r\n",
       "Q 20.796875 -1.421875 13.15625 6.1875 \r\n",
       "Q 5.515625 13.8125 5.515625 26.8125 \r\n",
       "Q 5.515625 40.234375 12.765625 48.109375 \r\n",
       "Q 20.015625 56 32.328125 56 \r\n",
       "Q 43.359375 56 49.78125 48.890625 \r\n",
       "Q 56.203125 41.796875 56.203125 29.59375 \r\n",
       "z\r\n",
       "M 47.21875 32.234375 \r\n",
       "Q 47.125 39.59375 43.09375 43.984375 \r\n",
       "Q 39.0625 48.390625 32.421875 48.390625 \r\n",
       "Q 24.90625 48.390625 20.390625 44.140625 \r\n",
       "Q 15.875 39.890625 15.1875 32.171875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-101\"/>\r\n",
       "      <path d=\"M 18.109375 8.203125 \r\n",
       "L 18.109375 -20.796875 \r\n",
       "L 9.078125 -20.796875 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.390625 \r\n",
       "Q 20.953125 51.265625 25.265625 53.625 \r\n",
       "Q 29.59375 56 35.59375 56 \r\n",
       "Q 45.5625 56 51.78125 48.09375 \r\n",
       "Q 58.015625 40.1875 58.015625 27.296875 \r\n",
       "Q 58.015625 14.40625 51.78125 6.484375 \r\n",
       "Q 45.5625 -1.421875 35.59375 -1.421875 \r\n",
       "Q 29.59375 -1.421875 25.265625 0.953125 \r\n",
       "Q 20.953125 3.328125 18.109375 8.203125 \r\n",
       "z\r\n",
       "M 48.6875 27.296875 \r\n",
       "Q 48.6875 37.203125 44.609375 42.84375 \r\n",
       "Q 40.53125 48.484375 33.40625 48.484375 \r\n",
       "Q 26.265625 48.484375 22.1875 42.84375 \r\n",
       "Q 18.109375 37.203125 18.109375 27.296875 \r\n",
       "Q 18.109375 17.390625 22.1875 11.75 \r\n",
       "Q 26.265625 6.109375 33.40625 6.109375 \r\n",
       "Q 40.53125 6.109375 44.609375 11.75 \r\n",
       "Q 48.6875 17.390625 48.6875 27.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-112\"/>\r\n",
       "      <path d=\"M 30.609375 48.390625 \r\n",
       "Q 23.390625 48.390625 19.1875 42.75 \r\n",
       "Q 14.984375 37.109375 14.984375 27.296875 \r\n",
       "Q 14.984375 17.484375 19.15625 11.84375 \r\n",
       "Q 23.34375 6.203125 30.609375 6.203125 \r\n",
       "Q 37.796875 6.203125 41.984375 11.859375 \r\n",
       "Q 46.1875 17.53125 46.1875 27.296875 \r\n",
       "Q 46.1875 37.015625 41.984375 42.703125 \r\n",
       "Q 37.796875 48.390625 30.609375 48.390625 \r\n",
       "z\r\n",
       "M 30.609375 56 \r\n",
       "Q 42.328125 56 49.015625 48.375 \r\n",
       "Q 55.71875 40.765625 55.71875 27.296875 \r\n",
       "Q 55.71875 13.875 49.015625 6.21875 \r\n",
       "Q 42.328125 -1.421875 30.609375 -1.421875 \r\n",
       "Q 18.84375 -1.421875 12.171875 6.21875 \r\n",
       "Q 5.515625 13.875 5.515625 27.296875 \r\n",
       "Q 5.515625 40.765625 12.171875 48.375 \r\n",
       "Q 18.84375 56 30.609375 56 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-111\"/>\r\n",
       "      <path d=\"M 48.78125 52.59375 \r\n",
       "L 48.78125 44.1875 \r\n",
       "Q 44.96875 46.296875 41.140625 47.34375 \r\n",
       "Q 37.3125 48.390625 33.40625 48.390625 \r\n",
       "Q 24.65625 48.390625 19.8125 42.84375 \r\n",
       "Q 14.984375 37.3125 14.984375 27.296875 \r\n",
       "Q 14.984375 17.28125 19.8125 11.734375 \r\n",
       "Q 24.65625 6.203125 33.40625 6.203125 \r\n",
       "Q 37.3125 6.203125 41.140625 7.25 \r\n",
       "Q 44.96875 8.296875 48.78125 10.40625 \r\n",
       "L 48.78125 2.09375 \r\n",
       "Q 45.015625 0.34375 40.984375 -0.53125 \r\n",
       "Q 36.96875 -1.421875 32.421875 -1.421875 \r\n",
       "Q 20.0625 -1.421875 12.78125 6.34375 \r\n",
       "Q 5.515625 14.109375 5.515625 27.296875 \r\n",
       "Q 5.515625 40.671875 12.859375 48.328125 \r\n",
       "Q 20.21875 56 33.015625 56 \r\n",
       "Q 37.15625 56 41.109375 55.140625 \r\n",
       "Q 45.0625 54.296875 48.78125 52.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-99\"/>\r\n",
       "      <path d=\"M 54.890625 33.015625 \r\n",
       "L 54.890625 0 \r\n",
       "L 45.90625 0 \r\n",
       "L 45.90625 32.71875 \r\n",
       "Q 45.90625 40.484375 42.875 44.328125 \r\n",
       "Q 39.84375 48.1875 33.796875 48.1875 \r\n",
       "Q 26.515625 48.1875 22.3125 43.546875 \r\n",
       "Q 18.109375 38.921875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 75.984375 \r\n",
       "L 18.109375 75.984375 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.34375 51.125 25.703125 53.5625 \r\n",
       "Q 30.078125 56 35.796875 56 \r\n",
       "Q 45.21875 56 50.046875 50.171875 \r\n",
       "Q 54.890625 44.34375 54.890625 33.015625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-104\"/>\r\n",
       "      <path d=\"M 44.28125 53.078125 \r\n",
       "L 44.28125 44.578125 \r\n",
       "Q 40.484375 46.53125 36.375 47.5 \r\n",
       "Q 32.28125 48.484375 27.875 48.484375 \r\n",
       "Q 21.1875 48.484375 17.84375 46.4375 \r\n",
       "Q 14.5 44.390625 14.5 40.28125 \r\n",
       "Q 14.5 37.15625 16.890625 35.375 \r\n",
       "Q 19.28125 33.59375 26.515625 31.984375 \r\n",
       "L 29.59375 31.296875 \r\n",
       "Q 39.15625 29.25 43.1875 25.515625 \r\n",
       "Q 47.21875 21.78125 47.21875 15.09375 \r\n",
       "Q 47.21875 7.46875 41.1875 3.015625 \r\n",
       "Q 35.15625 -1.421875 24.609375 -1.421875 \r\n",
       "Q 20.21875 -1.421875 15.453125 -0.5625 \r\n",
       "Q 10.6875 0.296875 5.421875 2 \r\n",
       "L 5.421875 11.28125 \r\n",
       "Q 10.40625 8.6875 15.234375 7.390625 \r\n",
       "Q 20.0625 6.109375 24.8125 6.109375 \r\n",
       "Q 31.15625 6.109375 34.5625 8.28125 \r\n",
       "Q 37.984375 10.453125 37.984375 14.40625 \r\n",
       "Q 37.984375 18.0625 35.515625 20.015625 \r\n",
       "Q 33.0625 21.96875 24.703125 23.78125 \r\n",
       "L 21.578125 24.515625 \r\n",
       "Q 13.234375 26.265625 9.515625 29.90625 \r\n",
       "Q 5.8125 33.546875 5.8125 39.890625 \r\n",
       "Q 5.8125 47.609375 11.28125 51.796875 \r\n",
       "Q 16.75 56 26.8125 56 \r\n",
       "Q 31.78125 56 36.171875 55.265625 \r\n",
       "Q 40.578125 54.546875 44.28125 53.078125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-115\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(125.295312 171.376563)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"61.523438\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "      <use x=\"125\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "      <use x=\"186.181641\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"241.162109\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "      <use x=\"304.541016\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_2\">\r\n",
       "    <g id=\"ytick_1\">\r\n",
       "     <g id=\"line2d_7\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L -3.5 0 \r\n",
       "\" id=\"mc9ec33caa5\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"45.478125\" xlink:href=\"#mc9ec33caa5\" y=\"64.096241\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_8\">\r\n",
       "      <!-- $\\mathdefault{10^{0}}$ -->\r\n",
       "      <g transform=\"translate(20.878125 67.89546)scale(0.1 -0.1)\">\r\n",
       "       <use transform=\"translate(0 0.765625)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use transform=\"translate(63.623047 0.765625)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use transform=\"translate(128.203125 39.046875)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_2\">\r\n",
       "     <g id=\"line2d_8\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L -2 0 \r\n",
       "\" id=\"m79e2ce54b1\" style=\"stroke:#000000;stroke-width:0.6;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"128.533753\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_3\">\r\n",
       "     <g id=\"line2d_9\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"112.300034\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_4\">\r\n",
       "     <g id=\"line2d_10\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"100.782027\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_5\">\r\n",
       "     <g id=\"line2d_11\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"91.847967\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_6\">\r\n",
       "     <g id=\"line2d_12\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"84.548308\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_7\">\r\n",
       "     <g id=\"line2d_13\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"78.376535\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_8\">\r\n",
       "     <g id=\"line2d_14\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"73.030301\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_9\">\r\n",
       "     <g id=\"line2d_15\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"68.314589\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_10\">\r\n",
       "     <g id=\"line2d_16\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"36.344515\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_11\">\r\n",
       "     <g id=\"line2d_17\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"20.110796\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_12\">\r\n",
       "     <g id=\"line2d_18\">\r\n",
       "      <g>\r\n",
       "       <use style=\"stroke:#000000;stroke-width:0.6;\" x=\"45.478125\" xlink:href=\"#m79e2ce54b1\" y=\"8.59279\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_9\">\r\n",
       "     <!-- rmse -->\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 41.109375 46.296875 \r\n",
       "Q 39.59375 47.171875 37.8125 47.578125 \r\n",
       "Q 36.03125 48 33.890625 48 \r\n",
       "Q 26.265625 48 22.1875 43.046875 \r\n",
       "Q 18.109375 38.09375 18.109375 28.8125 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 20.953125 51.171875 25.484375 53.578125 \r\n",
       "Q 30.03125 56 36.53125 56 \r\n",
       "Q 37.453125 56 38.578125 55.875 \r\n",
       "Q 39.703125 55.765625 41.0625 55.515625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-114\"/>\r\n",
       "      <path d=\"M 52 44.1875 \r\n",
       "Q 55.375 50.25 60.0625 53.125 \r\n",
       "Q 64.75 56 71.09375 56 \r\n",
       "Q 79.640625 56 84.28125 50.015625 \r\n",
       "Q 88.921875 44.046875 88.921875 33.015625 \r\n",
       "L 88.921875 0 \r\n",
       "L 79.890625 0 \r\n",
       "L 79.890625 32.71875 \r\n",
       "Q 79.890625 40.578125 77.09375 44.375 \r\n",
       "Q 74.3125 48.1875 68.609375 48.1875 \r\n",
       "Q 61.625 48.1875 57.5625 43.546875 \r\n",
       "Q 53.515625 38.921875 53.515625 30.90625 \r\n",
       "L 53.515625 0 \r\n",
       "L 44.484375 0 \r\n",
       "L 44.484375 32.71875 \r\n",
       "Q 44.484375 40.625 41.703125 44.40625 \r\n",
       "Q 38.921875 48.1875 33.109375 48.1875 \r\n",
       "Q 26.21875 48.1875 22.15625 43.53125 \r\n",
       "Q 18.109375 38.875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.1875 51.21875 25.484375 53.609375 \r\n",
       "Q 29.78125 56 35.6875 56 \r\n",
       "Q 41.65625 56 45.828125 52.96875 \r\n",
       "Q 50 49.953125 52 44.1875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-109\"/>\r\n",
       "     </defs>\r\n",
       "     <g transform=\"translate(14.798437 87.75625)rotate(-90)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"41.097656\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"138.509766\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "      <use x=\"190.609375\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"line2d_19\">\r\n",
       "    <path clip-path=\"url(#pc6fcaf29ac)\" d=\"M 54.355398 13.377273 \r\n",
       "L 56.148786 21.972269 \r\n",
       "L 57.942175 27.948325 \r\n",
       "L 59.735563 32.783329 \r\n",
       "L 61.528951 36.930987 \r\n",
       "L 63.32234 40.628189 \r\n",
       "L 65.115728 44.031064 \r\n",
       "L 66.909117 47.17041 \r\n",
       "L 68.702505 50.129121 \r\n",
       "L 70.495894 52.946976 \r\n",
       "L 72.289282 55.657156 \r\n",
       "L 74.08267 58.256162 \r\n",
       "L 75.876059 60.788064 \r\n",
       "L 77.669447 63.242213 \r\n",
       "L 79.462836 65.638259 \r\n",
       "L 81.256224 67.985598 \r\n",
       "L 83.049613 70.285463 \r\n",
       "L 84.843001 72.539269 \r\n",
       "L 86.636389 74.769743 \r\n",
       "L 88.429778 76.97259 \r\n",
       "L 90.223166 79.152408 \r\n",
       "L 92.016555 81.280398 \r\n",
       "L 93.809943 83.404158 \r\n",
       "L 95.603332 85.510493 \r\n",
       "L 97.39672 87.580841 \r\n",
       "L 99.190108 89.637963 \r\n",
       "L 100.983497 91.660339 \r\n",
       "L 102.776885 93.680016 \r\n",
       "L 104.570274 95.682103 \r\n",
       "L 106.363662 97.635402 \r\n",
       "L 108.157051 99.553138 \r\n",
       "L 109.950439 101.464767 \r\n",
       "L 111.743827 103.364176 \r\n",
       "L 113.537216 105.197894 \r\n",
       "L 115.330604 107.020975 \r\n",
       "L 117.123993 108.771623 \r\n",
       "L 118.917381 110.52655 \r\n",
       "L 120.71077 112.206729 \r\n",
       "L 122.504158 113.868253 \r\n",
       "L 124.297546 115.440467 \r\n",
       "L 126.090935 116.961447 \r\n",
       "L 127.884323 118.419896 \r\n",
       "L 129.677712 119.841933 \r\n",
       "L 131.4711 121.193984 \r\n",
       "L 133.264489 122.490325 \r\n",
       "L 135.057877 123.701455 \r\n",
       "L 136.851265 124.84339 \r\n",
       "L 138.644654 125.926994 \r\n",
       "L 140.438042 126.909288 \r\n",
       "L 142.231431 127.859582 \r\n",
       "L 144.024819 128.720257 \r\n",
       "L 145.818208 129.513623 \r\n",
       "L 147.611596 130.254372 \r\n",
       "L 149.404985 130.909574 \r\n",
       "L 151.198373 131.515912 \r\n",
       "L 152.991761 132.053686 \r\n",
       "L 154.78515 132.554806 \r\n",
       "L 156.578538 133.002672 \r\n",
       "L 158.371927 133.401061 \r\n",
       "L 160.165315 133.739725 \r\n",
       "L 161.958704 134.056064 \r\n",
       "L 163.752092 134.337557 \r\n",
       "L 165.54548 134.58532 \r\n",
       "L 167.338869 134.801373 \r\n",
       "L 169.132257 134.993026 \r\n",
       "L 170.925646 135.154428 \r\n",
       "L 172.719034 135.303633 \r\n",
       "L 174.512423 135.422231 \r\n",
       "L 176.305811 135.533179 \r\n",
       "L 178.099199 135.631885 \r\n",
       "L 179.892588 135.704419 \r\n",
       "L 181.685976 135.773961 \r\n",
       "L 183.479365 135.829115 \r\n",
       "L 185.272753 135.884442 \r\n",
       "L 187.066142 135.918861 \r\n",
       "L 188.85953 135.971966 \r\n",
       "L 190.652918 136.009779 \r\n",
       "L 192.446307 136.005292 \r\n",
       "L 194.239695 136.058523 \r\n",
       "L 196.033084 136.041658 \r\n",
       "L 197.826472 136.080164 \r\n",
       "L 199.619861 136.090974 \r\n",
       "L 201.413249 136.135431 \r\n",
       "L 203.206637 136.16078 \r\n",
       "L 205.000026 136.149983 \r\n",
       "L 206.793414 136.201203 \r\n",
       "L 208.586803 136.24051 \r\n",
       "L 210.380191 136.282291 \r\n",
       "L 212.17358 136.286234 \r\n",
       "L 213.966968 136.324095 \r\n",
       "L 215.760356 136.363254 \r\n",
       "L 217.553745 136.393256 \r\n",
       "L 219.347133 136.422811 \r\n",
       "L 221.140522 136.484335 \r\n",
       "L 222.93391 136.533354 \r\n",
       "L 224.727299 136.582974 \r\n",
       "L 226.520687 136.656903 \r\n",
       "L 228.314075 136.720427 \r\n",
       "L 230.107464 136.829542 \r\n",
       "L 231.900852 136.922727 \r\n",
       "\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_3\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 45.478125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path d=\"M 240.778125 143.1 \r\n",
       "L 240.778125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_5\">\r\n",
       "    <path d=\"M 45.478125 143.1 \r\n",
       "L 240.778125 143.1 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_6\">\r\n",
       "    <path d=\"M 45.478125 7.2 \r\n",
       "L 240.778125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"pc6fcaf29ac\">\r\n",
       "   <rect height=\"135.9\" width=\"195.3\" x=\"45.478125\" y=\"7.2\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测并在Kaggle提交结果\n",
    "\n",
    "def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):\n",
    "    net = get_net(train_features.shape[1])\n",
    "    train_ls,_ = train(net, train_features, train_labels, None, None, num_epochs, lr, weight_decay, batch_size)\n",
    "    semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'rmse')\n",
    "    print('train rmse %f' % train_ls[-1])\n",
    "    \n",
    "    preds = net(test_features).detach().numpy()\n",
    "    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)\n",
    "    print(submission)\n",
    "\n",
    "train_and_pred(features_train, features_test, labels_train, test_data, num_epochs, lr, weight_decay, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 小结\n",
    "1、通常需要对真实数据做预处理\n",
    "\n",
    "2、可以使用 K 折交叉验证来选择模型并调节参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "yczlab_3.6",
   "language": "python",
   "name": "yczlab_python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
